Clinically Relevant Gait Biomechanics Improvements After Total Knee Arthroplasty
Bibliographic record
Abstract
Improved joint function is a primary goal of total knee arthroplasty (TKA), yet persistent postoperative deficits in joint biomechanics are common, and the link between these and other clinical outcomes is poorly understood. Evidence on clinically meaningful gait outcomes after arthroplasty is limited, hindering their uptake in clinical trials and technological innovations in arthroplasty. This paper examined the associations between gait outcomes and satisfaction after TKA and defined minimal detectable and clinically meaningful thresholds for gait outcome changes. Thirty-one patients underwent instrumented gait analysis immediately before and 1 year after TKA and were categorized as high (>90) and lower (≤90) satisfaction with their joint replacement after surgery. Sixteen (51.6%) patients self-reported high satisfaction, and 15 (48.4%) reported lower satisfaction. There were no pre-TKA gait differences between the groups, but higher satisfaction postarthroplasty was associated with more a biphasic pattern improvement of the knee flexion/extension moment during stance from presurgery to postsurgery (r = .59, P = .001). Most patients, however, did not achieve minimal detectable or meaningful clinically important improvements in knee biomechanics from pre-TKA to post-TKA. The established thresholds for meaningful improvement in biomechanical variables may be used in future studies to relate measures of patient satisfaction to objective gait outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".